Age-(in)dependent altered molecular mechanisms in Parkinson's disease through extracellular vesicle proteome and lipidome.

Parkinson's disease (PD) remains insidious and clinically elusive at early stages due to the lack of precise, non-invasive biomarkers. Given their ability to cross the blood-brain barrier, extracellular vesicles (EVs) offer a promising platform for biomarker discovery in neurodegeneration. Using an affinity-based EV isolation method, we profile EV proteomes and lipidomes from plasma across life stages, followed by targeted validation via parallel reaction monitoring (PRM). We identify both age-independent and age-dependent EV biomarkers predictive of prodromal PD and capable of distinguishing PD from multiple system atrophy (MSA). Functional analyses reveal protein-lipid interactions contributing to PD pathophysiology, including lipid-mediated modulation of APOE signaling. Machine learning models integrating putative EV proteins achieve robust classification across age groups. Our findings demonstrate the diagnostic potential of EV-derived molecules for early PD detection and mechanistic insights, advancing a clinically applicable, non-invasive strategy for risk stratification and disease intervention.